Complex Spatio-Temporal Distribution and Genogeographic Affinity of Mitochondrial DNA Haplogroups in 24,216 Danes

Home , Haplogroup T (mtDNA)

bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Complex spatio-temporal distribution and genogeographic affinity of mitochondrial DNA haplogroups in 24,216 Danes

Jonas Bybjerg-Grauholm1*, Christian M Hagen1*, Vanessa F Gonçalves2, Marie Bækvad-Hansen1, Christine S Hansen1, Paula L Hedley1, Jørgen K Kanters3, Jimmi Nielsen4, Michael Theisen1, Ole Mors5, James Kennedy2, Thomas D Als6, Alfonso B Demur7, Thomas M Werge7, Merete Nordentoft8, Anders Børglum6, Preben Bo Mortensen9, David M Hougaard1 & Michael Christiansen1,3#

1) Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark 2) Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada 3) Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark 4) Aalborg Psychiatric Hospital. Aalborg University Hospital, Aalborg, Denmark 5) Department of Clinical Medicine, Aarhus University, Århus, Denmark 6) Institute of Medical Genetics, Aarhus University, Århus, Denmark 7) Mental Health Centre, Sct Hans, Capital Region of Denmark, Denmark 8) Mental Health Centre, Capital Region of Denmark, Denmark 9) Center for Register Research, Institute of Economics, Aarhus University, Århus, Denmark The study was conducted under the auspices of the iPSYCH study (www.iPSYCH.au.dk) *JG and CMH contributed equally to the study. Key words: mitochondrial DNA, haplogroup, population genetics, energy metabolism

Running title: mtDNA haplogroups in 24,216 Danes

#Correspondence: Professor, chief physician, Michael Christiansen, FRCPath, MD Department for Congenital Disorders, Statens Serum Institut And Department of Biomedical Sciences, University of Copenhagen. E-mail: [email protected]; Phone: +4520720463.

Abstract

Mitochondrial DNA (mtDNA) haplogroups (hgs) are evolutionarily conserved sets of mtDNA SNP-

haplotypes with characteristic geographical distribution. Associations of hgs with disease and

physiological characteristics have been reported, but have frequently not been reproducible. Using

418 mtDNA SNPs on the PsychChip (Illumina), we assessed the spatio-temporal distribution of

mtDNA hgs in Denmark in DNA isolated from 24,642 geographically un-biased dried blood spots

(DBS), collected from 1981 to 2005 through the Danish National Neonatal Screening program.

Geno-geographic affinity (ancestry background) was established with ADMIXTURE using a reference

of 100K+ autosomal SNPs in 2,248 individuals from nine populations. The hg distribution was

typically Northern European, and hgs were highly variable based on median-joining analysis,

suggesting multiple founder events. Considerable heterogeneity and variation in autosomal geno-

geographic affinity was observed. Thus, individuals with hg H exhibited 95 %, and U hgs 38.2 % -

92.5 %, Danish ancestry. Significant clines between geographical regions and rural and metropolitan populations were found. Over 25 years, macro-hg L increased from 0.2 % to 1.2 % (p = 1.1*E-10),

and M from 1 % to 2.4 % (p = 3.7*E-8). Hg U increased among the R macro-hg from 14.1 % to 16.5

% (p = 1.9*E-3). Geno-geographic affinity, geographical skewedness, and sub-hg distribution

suggested that the L, M and U increases are due to immigration. The complex spatio-temporal dynamics and geno-geographic heterogeneity of mtDNA in the Danish population reflect repeated migratory events and, in later years, net immigration. Such complexity may explain the often contradictory and population-specific reports of mito-genomic association with disease.

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Introduction

Mitochondria are subcellular organelles responsible for oxidative phosphorylation (OXPHOS), producing ~ 80% of the ATP in eukaryotic cells1, apoptosis and cell-cycle regulation2, redox- and calcium homeostasis3 as well as intracellular signaling4. Each mitochondrion contains 2 - 10 copies of a 16.6 kb double-stranded mtDNA containing 37 genes5. Thirteen genes code for proteins in the five enzyme complexes conducting OXPHOS, whereas twenty-two genes code for tRNAs and two for rRNAs, all involved in intra-mitochondrial translation6. The mitochondrial proteome comprises approximately 1200 proteins7; 8, of which mtDNA genes encode ~ 1%. The mtDNA is maternally inherited9, exhibits a high mutation rate10, and does not undergo recombination5. Genetic variants in mtDNA – as well as variants in the nuclear genes encoding the mitochondrial proteome - have been associated with disease11; 12 13. More than 150 mitochondrial syndromes14 have been associated with more than 300 variants7; 15.

Geographically and population specific lineages of mtDNA, haplogroups (hgs), have become fixed17, through the processes of random genetic drift and selection as the human populations dispersed throughout the world16. The advent of high throughput DNA sequencing technology, as well as implementation of biobanking technologies18, has enabled the construction of a high-resolution phylogenetic matrilineal mtDNA tree, Figure 119.

Mitochondrial hgs have been assigned a role as disease modifiers5. Particularly in neurological degenerative diseases such as Alzheimer’s disease 20-23 and Parkinson’s disease 23-25, but also in psychiatric disease26 and cardiac diseases such as hypertrophic27; 28 and ischemic cardiomyopathy29.

Supporting a role as disease modifiers, some mtDNA hgs have specific physiological characteristics,

e.g. reduced or increased ATP synthesis rates30; 31, and variation in methylation status of genes

involved in inflammation and signaling32; 33. The association of mtDNA SNPs and hgs with both diseases and functional characteristics of mitochondria, has led to a pathogenic paradigm34 where variation in mitochondrial function is considered to be of paramount importance for development of disease. Specific hgs have also been associated with longevity35 and likelihood of being engaged

in endurance athletic activities36. The clinical presentation of diseases caused by specific mtDNA

variants depends, in some cases, on the hg background37. However, some of these studies are

contradictory, either because they have been too poorly powered38, have not been carefully

stratified with respect to sex, age, geographical background39 or population admixture40, or have

used small areas of recruitment risking “occult” founder effects41. To circumvent some of these

problems a recent large study on mtDNA SNPs identified a number of SNPs that were associated

with several degenerative diseases42, however, the study pooled sequence information from a large

geographical area, without correcting for potential population sub-structure.

Most countries have a complex history with repeated migrations43 and several bottle-necks caused

by disease, war and emigration44; 45. These demographic events are reflected in the fine scale

genetic structure within sub-populations46. However, the significance of such events to

countrywide mtDNA hg distribution has not yet been assessed. As mtDNA interact functionally with

the nuclear genome, it is paramount to ensure that specific mtDNA hgs – which are a marker of matrilineal genetic origin – do not represent population sub-structure at the genomic level. In theory, mtDNA is inherited independently of the nuclear genome, but population admixture and

geographic isolation may result in linkage disequilibrium between mtDNA and the nuclear genome.

Such a linkage disequilibrium might interfere with genetic association analysis.

Here we demonstrate the complexity of the spatio-temporal dynamics and genogeographic affinity

of mtDNA hgs in 24,216 Danes, which were sampled at birth during a 25-year period. This number

represents 1.6 % of the population. The sampling material was dried blood spots (DBSs) obtained as

part of the Danish Neonatal Screening Program47, the very nature of which makes sampling

geographically un-biased. Array analysis was performed using the PsychChip (Illumina, CA, USA) typing 588,454 variants.

5 bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Materials and Methods

Ethics statement This is a register-based cohort study solely using data from national health registries. The study was approved by the Scientific Ethics Committees of the Central Denmark Region (www.komite.rm.dk)

(J.nr.: 1-10-72-287-12) and executed according to guidelines from the Danish Data Protection

Agency (www.datatilsynet.dk) (J.nr.: 2012-41-0110). Passive consent was obtained, in accordance with Danish Law nr. 593 of June 14, 2011, para 10, on the scientific ethics administration of projects within health research. Permission to use the DBS samples stored in the Danish Neonatal Screening

Biobank (DNSB) was granted by the steering committee of DNSB (SEP 2012/BNP).

Persons As part of the iPSYCH (www.iPSYCH.au.dk) recruitment protocol, 24,651 singletons (47.1 % female), born between May 1 1981 and Dec 31 2005 were selected at random from the Danish Central

Person Registry. The singletons had to have been alive one year after birth, and to have a mother registered in the Danish Central Person Registry. Furthermore, it should be possible to extract DNA from the DBS. DBS cards were obtained from the Danish Neonatal Screening Biobank at Statens

Serum Institut 48 and DNA was extracted and analysed as described below. At the time of analysis

(2012), the mean age of females was 18.2 years (SD: 6.6 years) and for males 18.8 years (SD: 6.7 years). There was no bias in the geographical distribution of the birthplace of samples, Figure 2.

Genetic analysis

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From each DBS card two 3.2-mm disks were excised from which DNA extracted using Extract-N-

Amp Blood PCR Kit (Sigma-Aldrich, St Louis, MO, USA)(extraction volume: 200 μL). The extracted

DNA samples were whole genome amplified (WGA) in triplicate using the REPLIg kit (Qiagen,

Hilden, Germany), then pooled into a single aliquot. Finally, WGA DNA concentrations were estimated using the Quant-IT Picogreen dsDNA kit (Invitrogen, Carlsbad, CA, USA). The amplified samples were genotyped at the Broad Institute (MA, USA) using the Psychiatric Genetic Consortia developed PsychChip (Illumina, CA, USA) typing 588,454 variants. Following genotyping, samples with less than 97% call rate, as well as those where the estimated gender differed from the expected gender were removed from further analysis; altogether 435 (1.8 %) samples were removed due to problems with calling mtDNA variants. We then isolated the 418 mitochondrial loci and reviewed the genotype calls, before exporting into the PED/MAP format using GenomeStudio

(Illumina, CA, USA). Samples were loaded into GenomeStudio (version 2011.a), a custom cluster was created using Gentrain (version 2), following automatic clustering all positions with heterozygotes were manually curated. The data was exported relative to the forward strand using

PLINK Input Report Plug-in (version 2.1.3). Eigenvectors were calculated using PLINK (v1.90b3.31).

PCA plots were created using the package ggplot2 (version 1.0.1) in R (version 3.1.3).

mtDNA SNPing Haplotyping of mtDNA was performed manually using the defining SNPs reported in www.phylotree.org 19. Hierarchical affiliation to macro-hg i.e. L0 – L6, M, N, R, and subsequent to hgs – units more distal in the cladogram, Figure 1, was performed. In some cases it was possible to establish affiliation to even sub-hgs. The call efficiencies of SNPs used in defining haplo- and sub- haplogroup affiliation are summarized in Suppl. Table 1.

Phylogenetic analyses Phylogenetic analyses was performed by constructing median-joining networks with Network

4.6.1.3 (http://www.fluxus-engineering.com). Fasta converted sequences of mtDNA SNPs from each person were aligned, sequences were pre-processed with Star Contraction (Maximum star

radius 5 for R, N, M and 1 for L), then Median Joining networks were constructed (using the

network parameters: Epsilon 10, Frequency >1, active) followed by post-processing with a

maximum parsimony algorithm (MP) 49; 50. Network Publisher were used to post-process the

networks51.

Genogeographic affinity (nuclear genomic ancestry) analysis Ancestry estimation was done using ADMIXTURE 1.3.052. Briefly, a reference population consisting

of Human Genome Diversity Project (HGDP) (http://www.hagsc.org/hgdp/) genotyping SNP data

set, supplemented with representative samples of danes (716 individuals) and greenlanders (592

individuals) available at SSI from unrelated projects, was used. The final reference data set

consisted of 103,268 SNPs and 2,248 individuals assigned to one of nine population groups: Africa,

America, Central South Asia, Denmark, East Asia, non-Danish Europe, Greenland, Middle East and

Oceania. K – number of clusters defined - was set to eight, based on principal component analysis clustering (data not shown).

Individuals belonging to individual mtDNA hgs or sub-hgs were merged with the reference

population data set and analysed using ADMIXTURE. For prediction of the ancestry of individuals

within the mtDNA hgs we created a random forest model53 based on the reference data set, with

the clusters Q1-8 as predictors and population groups as outcome. The prediction was thus

supervised. Prediction was done in R version 3.2.2, using the Caret package. The distribution of the

eight basic clusters in samples of different geographical origin is shown in Suppl Figure 1. As

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expected, the African-characteristic cluster distribution plays a decreasing role when going from

Africa over the Middle East to Central South Asia. Likewise, the Danish cluster distribution is very similar to that of Europe.

Statistics

The statistical significance of differences in mtDNA proportions was assessed using a permutation version of Fisher’s exact test54. Calculations were performed using R 55. To assess population stratification56, principal component analysis (PCA) was performed.

Results

Distribution of macro-haplogroups The mtDNA macro-hg distribution pattern is typically northern European57 with > 90% belonging to the R macro-hg, 7.0 % belonging to N and 1.6 % to M of likely Near Eastern or Asian origin57 and 0.7

% belonging to the combined L macro-hgs (L0-L6) of a likely African origin58, Table 1. A PCA analysis

(PC 1 versus PC 2) based on all the SNPs showed a clear separation between R and L, with N and M

located intermediately, figure 3A. The PC1 seems to reflect time since branching, whereas PC2

reflects geographical distance.

Distribution of haplogroups

The R macro-hg was dissolved into hgs as shown in Table 2. However, it was not possible, with the available SNPs, to differentiate the HV and P hgs from the R macro-hg (Suppl. Table 1, for details).

Principle Component 1 versus 2, Figure 3B, demonstrated a clear clustering of mtDNA SNPs from persons belonging to each hg. The proximity of the clusters comprising U and K and H and V, respectively, is in accordance with the current phylogenetic mtDNA tree, Figure 1. Likewise, the median-joining graph of the R macro-hg, Figure 4A, based on all the called SNPs, disclosed a phylogenetic relationship compatible with that of Figure 1. In addition, all the hgs exhibit a considerable complexity, suggesting that each hg is the result of multiple migratory events and thus, multiple founder events. The sub-hg distribution of each of the hgs of the R macro-hg is shown in Table 3.

The L, N and M macro-hgs were infrequent, Table 1. M and N could be broken down into hgs as shown in Table 4. The complexity of these macro-hgs, and macro-hg L, as demonstrated by median-

joining phylogenetic analysis, Figure 4 B, C and D, suggest that these macro-hgs are composed of specific haplotypes from multiple immigrations over time. The number of DNA variations between individual haplotypes is so large that they can not represent development from a single founder within the time frame defined by the length of time in which Denmark has been populated.

Geno-geographical affinity of mtDNA haplogroups

An admixture analysis of the persons of different hgs was performed with results as shown in Table

5. The major hgs, H and its sub-hgs, have a 90-95% Danish ancestry and ~ 5% non-Danish European

structure. However, there is a great variation between mtDNA hgs – most pronounced in hg U -,

and most of the hgs have a notable – but varying – proportion of admixture from Europe, Middle

East and Central South Asia, Table 5. This finding is compatible with the very complex M-J diagram

of the Danish hgs, Figure 4A. The ancestry or geno-geographic affinity of the macro-hgs M and N

differ even more, Table 5, with the M being of South East and East Asian affinity, and N of Danish

and European affinity. Macro-hg L exhibited – surprisingly – a predominance of Middle Eastern and

European genomic affinity, Table 5, where an African ancestry should be expected57; 59.

Spatial distribution of mtDNA haplogroups

Denmark is divided into five geographical and administrative regions as shown in Figure 2. The

samples for this study were obtained from all regions in Denmark and linked to the postal code of

the birthplace. The frequency of the macro-hgs and the most frequent hgs is shown for each

administrative region in Table 6. The most marked differences were the relatively low frequency of

hg H, 43.4 %, in the Capital Region, compared to the North Denmark Region with 48.8 % hg H (p <

0.0001), and the higher frequencies of hgs L, M and R (in total 7.5 %) in the Capital Region

compared to the North Denmark Region (in total 3.9 %) (p < 0.0001). Thus, in the Capital Region, 11

the L hgs have a frequency of 1.3 %, compared with 0.4 – 0.6 % in the other regions and the M hgs have a frequency of 2.6 % in the Capital region compared to 1.0 – 1.6 % in the other regions. As the

L and M hgs are rare in the European population and very frequent in African and Asian populations, the noted difference probably reflects a higher proportion and preferential localization of non-ethnic Danes in the Capital region. A similar spatial difference was apparent when the hg distributions of persons from the Danish metropolitan areas, comprising the major cities,

Copenhagen, Aarhus, Odense and Aalborg, were compared with the distributions in the remaining rural areas, Suppl Table 2. In the metropolitan areas, the combined frequency of L and M hhgs was

4.4 % as compared to 1.9 % in the rural areas.

Temporal distribution of mtDNA haplogroups

The frequency of the major hgs H, J, T, K, V did not change significantly from year to year in the period from 1981 to 2005 (Results not shown).

The frequencies of M and L hgs, Figure 5, increased significantly over the period. The L hgs increased from a constant level of ~ 0.4 % from 1981 – 1995 to ~ 1.5 % in 2005. The M hgs rose from a constant level of ~ 1% from 1981 – 1991 to ~ 3 % in 2005. The proportion of L and M hgs increased significantly from the period 1981-1986 to the period 2000-2005, Table 7. The considerable diversity of the M macro-hg, Figure 4C and the extreme diversity of the L macro-hg,

Figure 4D, as disclosed by the M-J-network, suggests that the hgs are the result of immigration from different source populations. The occurrence of new clusters on the PCA when the macro-hgs M and L from the period 1981-1986 were compared with the same macro-hgs in 2000-2005, Suppl.

Figs. 2A & B, makes it likely that the increase in the proportion of both macro-hgs represent immigration.

The hg U also increased in proportion each year from 1981 to 2005, Figure 5, albeit not significantly.

However, when analysing only the R-macro-hg, the proportion of hg-U increased significantly (data not shown). In the case of the U-hg, the PCA did not reveal the appearance of novel clusters when

comparing 1981-1986 with 2000-2005, Suppl Fig 2C. Thus, there was no evidence for the

introduction of novel U-hgs over the period. However, a more detailed analysis revealed that the

increase in U-hgs was largely due to an increase in the infrequent sub-hgs U1, U6, U7, and U8, Suppl

Table 3, as all these increased by more than 40%. An admixture analysis showed that whereas the

Danish autosomal genomic affinity of the hgs U*, U2-U3, U4-9, U5a&b is in the range of 80.6% -

92.5 %, Table 5, comparable to that of the other major European hgs, the Danish affinity drops to

36.4% - 42.3 % for U1, U6 and U7. According to Table 5, the U1 and U7 have strong Middle Eastern

(29.4% and 13.6%) and Central South Asian (14.7% and 39.8%) autosomal geno-geographic

affinities, and the U6 exhibits a very strong Middle Eastern autosomal affinity (53.8%). This suggests

that the increase in frequency of hg U is largely due to expansion of hgs brought into Denmark as a

result of recent immigration.

Discussion

This study shows that the distribution of mtDNA hgs in Denmark is highly dynamic and complex. It comprises 1.6 % of the Danish population over a 25-year period, and is by far the largest performed

of the distribution of mtDNA hgs in any country. The method of collecting stored DBS from the PKU

biobank, where the coverage is ~ 99 % 60, enabled us to survey a true population based sample of

mtDNA from persons, where the time and place of birth was known from national electronic

registries. This is in contrast to most other population genetic studies of adults sampled in a specific bias-prone context, e.g. hospitalized patients or geographically biased samplings.

The distribution of macro-hgs, Table 1, is typical of Northern Europe61; 62. The large majority of

persons had hgs belonging to the R macro-hg, Table 2. The distribution of hgs within the R macro-

hg, Table 2, is very similar to that previously described among 9000+ persons from the greater

Copenhagen area63 and a much smaller (~200 cases) Danish forensic control sample64, as well as

that found in 2000 Danish exomes from a mixed control and diabetes population65. However, the

latter study is seriously biased as it contains ~ 50% patients with type-2 diabetes, which have shown

associations with mtDNA haplotypes65. Despite this, the sub-hg distribution in macro-hg R is similar

to that seen in the present study, Table 3, e.g. H1 and H3 constitute 37.6 % and 6.5 % of the H hgs

in our study and 39.6 % and 5.2 % in the Li et al. study65.

The mtDNA haplotyping was based on array data with only 418 mtDNA SNPs and as a consequence

not all sub-hgs could be called. Ideally sub-haplotyping could be refined – without including sub-hg

defining SNPs from Phylotree – by the application of clustering approaches. However, this was not

attempted, as it would lead to results that could not be compared with other studies. The stringent

adherence to specific SNPs meant that a number of persons, normally in the order of 1-2% (see

tables 2-4), were not assigned to a specific haplotype. An advantage of using a limited set of

markers is that confounding due to private variants is avoided.

The complexity of each of the major hgs, as seen from the M-J-networks, Fig 4B-D, with multiple

nodes and a span of many mutations between different leaves suggests that the hgs are not

representative of single early founder event, as one mtDNA mutation is expected to occur per 3,000 yrs66. A notable exception is hg A, with a total prevalence of ~ 0.5 %, where the largest node, figure

4B, is associated with “daughter” nodes only 1-2 mtDNA mutations from the major node. It was only possible to identify the sub-hgs A1 and A5, and they only constituted a small fraction of the total A hg. A likely source of the A hgs is the Inuit population from Greenland, that is now a self-

ruling part of the Kingdom of Denmark. Several studies have established that the major hgs in

Greenlandic Inuits is A2 followed by hg D3 67-70, whereas other Inuit populations have other

characteristic hgs70; 71.

The lack of recombination, high mutation frequency, and fixation of mtDNA hgs has enabled the

use of mtDNA in population genetics to study population ancestry, migrations, gene flow and

genetic structure72; 73. Thus, populations on different continents, e.g. Native Americans74, Africans75

and Europeans76 were ascribed specific matrilineal mtDNA hg distributions62; 77. However, recent

studies using autosomal SNP markers have disclosed a considerable ancestral complexity

underlying an mtDNA classification in specific admixed populations, and the prediction of a specific

mtDNA hgs is not possible from a specific continental ancestry based on nuclear genetic markers78.

A problem with genetic analysis of admixed populations is the lack of temporal resolution. It is

often not possible to date a specific population split because differentiation between new and old migrations is impossible. Recent advances in sequencing ancient genomes79 have made it possible to combine genetic information from ancient humans with archaeological information on the age of skeletal remains80, thus constructing hg distribution maps with a temporal dimension for e.g. Ice age and Bronze age81; 82 Europe. Maps, that may help explain demic and cultural exchange70; 83-85.

There is no solid evidence of the presence of humans in Denmark 86 87 prior to the Last Glacial

Maximum (LGM)88 (26.5 – 19 kYBP). At that time Denmark was covered in ice, except for the southwestern part of Jutland89, and following the retraction of the ice sheath, peopling became possible

from the south89-92. The first inhabitants documented were late Paleolithic hunters entering from

southern Europe93. These hunters are discernible from Bølling time89 around 12,800 BC. The

archaeological remains suggest their transient presence in seasonal hunting periods94 until the

Mesolithic around 9,700 BC. The Hamburgian, Federmesser, Bromme and Ahrensburgian material

cultures89; 95, well known from findings in Germany89, are represented. The earliest anatomically

normal humans (ANM), present from around 45 – 41.5 kYBP96-98, where ancient DNA studies have

revealed the presence of mtDNA hgs M and pre-U299, have little similarity to present-day

Europeans. However the Europeans from around 37 kYBP to 14 kYBP have left their mark on

present-day Europeans82. From 14 kYBP the European population has a strong near-eastern

component82. Around 7 kYBP the Neolithic transformation gradually started as the result of a

demic dissemination of Neolithic Aegeans100. Thus, a minimum of three ancestral populations, i.e. a

western European hunter-gatherer, an ancient north Eurasian, and an early European farmer

population are needed to explain present-day European autosomal genome compositions 101. In

Denmark, where Paleolithic ancient genetic data have not been published, ancient mtDNA

haplotyping of three Neolithic corpses from 4.2 – 4 kYBP revealed two U4 and one U5a mtDNA hgs102 and later samples showed a mixture of hgs also found in northern Germany99; 102. The

presence of mtDNA clades deriving from Paleolithic and Neolithic Europeans in the extant Danish

population is thus explained.

In the Bronze age, an influx of people from the Russian steppe and North Caucasus, bringing the

Indo-European language and culture103, resulted in the last major prehistoric demic change81 in

Europe. In historic times the migrations have been many, particularly in the half-millennium

following the fall of the Western Roman Empire104. In Denmark, apart from continuous demic

exchange with Southern Scandinavia and present-day Germany89; 92, early historic time was mostly

characterized by emigration, i.e. the Heruli, Cimbriae and Teutones, Burgundians and Vikings91; 92.

The first census held in 1769 AD reported the population of what is present day Denmark to be ca

800,000 persons105. This number has increased to 5.6 mio in 2014, despite emigration of 287,000 persons between 1867 and 1914 (~10% of the population) largely to Northern America, and the same number from 1914 to 1968106. After the Second World War, Denmark has seen a considerable

immigration from other European countries, but also from Asian, Middle Eastern and African

countries, Suppl Figure 3. This part of Danish history can explain the occurrence of a diversity of

mtDNA clades deriving from a large number of geographic regions. The extant distribution of

mtDNA hgs reported here, Tables 1-4, reflect the complicated and heterogeneous origins of the

people currently inhabiting Denmark.

The distribution of H sub-hgs, Table 3, and the M-J graph of the H-hg, Figure 4A, with multiple

major nodes and 5-10 variants between leaves, suggest that the H-hg lineages are the result of

repeated immigrations. Most likely from northern Europe, where a study of 39 prehistoric hg H samples has shown that the distribution of hg H differed between early and middle-to-late Neolithic groupings. Prior to Neolithicum H hgs have not been found in skeletal remains; an ensemble of

Swedish Mesolithic hunter-gatherers all had U-hgs83. Whereas H, H5 and H1 were found throughout Neolithicum, H5b, H10, H16, H23, H26, H46, H88, and H89 were seen in early Neolithic samples, and H2, H3, H4, H5a, H6, H7, H11, H13, H82, and H90 in middle-to-late Neolithic samples107. The extant Danish H-hg distribution is compatible with contributions from throughout

Neolithicum, but estimating when the hgs appeared in Denmark remains unfeasible, as the distribution is similar to that of northern Germany. However, not all carriers of an H-hg have a

Danish autosomal geno-geographic affinity, table 5, compatible with a considerable recent immigration from countries with a European mtDNA distribution, Suppl Figure 3.

The most frequent Danish U-sub-hg is U5, Table 3, which is an old European mtDNA hg, with two major subclades, U5a and U5b, with coalescence time estimates of 16 – 20 kYBP and 20 – 24 kYBP, respectively108. The U5-hg is the most frequent U-sub-hg after LGM99 and the carriers have a Danish

autosomal genomic ancestry around 90 %, and a European ancestry of 6.1 – 8.8%, Table 5.

However, several of the less frequent U-sub-hgs have a considerable, ~ 40 – 60 % non-Danish and

non-European geno-geographic affinity based on autosomal markers, with U7 related to South East

Asia and U6 to the Middle East, respectively. In addition, J & T hgs, Figure 5, exhibit a relatively

strong non-Danish autosomal genomic ancestry. The M-J graphs, Figure 4A, also disclose a

considerable variation, much larger than could be attained merely within the timeframe where

Denmark has been populated. This may be explained by an extensive immigration, and for U6 and

U7 in very recent time. This is also compatible with the rising frequency of these hgs during the 25- year study period, Figure 6 and Suppl Table 3.

The N-macro-hg exhibits a high (>90%) combined Danish and European genomic affinity, Table 5, suggesting that the major part has been in Denmark for long. The major N-hgs are the I-hg, which has been found in meso- and neolithic Scandinavians102 and hg-X109 and hg-W, Table 4, and they

exhibit, figure 4B, a very extensive heterogeneity. All three hgs are old and have a broad, low

frequency, distribution in western Eurasia61, resulting from migratory events from the Near East and Central Asia. These events, viz. the significance for the presence of the hgs in Denmark, can not be temporally resolved.

Macro-hg-M, albeit infrequent (1.6 %), Table 1, has also increased in frequency recently, Figure 6,

and exhibits extensive heterogeneity, Figure 4C, in the M-J graph, suggesting many migratory

events of diverse origins. The largest group is M, that could not be decomposed further, but the

second most frequent, D4 (14.2% of M-hgs), is most likely of central Asian origin110, and the M2,

M3, M4 and M6 hgs, constituting 12.5% of M-hgs, are of Indian or Pakistani origin111. These findings

are compatible with the high genomic affinity (42.2%, Table 5) towards Central South Asia, and a recent entry to Denmark. There is a propensity for location in Metropolitan areas, Suppl Table 2, and the Capital region, Table 6, also compatible with recent immigration.

The low, but increasing, frequency of L-hgs (Table 1, Table 7 and Figure 6) predominantly L2 and L3

(Table 1), is also the result of multiple immigration events, as the M-J plot reveals an extensive

heterogeneity (Figure 4D). L2 and L3 hgs are frequent in south, west or east Africa, whereas the contribution from central Africa is small112; 113. However, the admixture analysis, Table 5, suggests a

much stronger autosomal genomic affinity to the Middle East (70.8 %) than to Africa (8.4%).

However, this finding could be an artefact of the ADMIXTURE analysis, as the highly variable African

genomic ancestry59 114 is represented by only 127 genomes (~ 6 % of the total reference set – see

Materials and Methods), as compared to 178 genomes from the Middle East, making the definition of African ancestry imprecise. Alternatively, it could be caused by the extensive demic exchange that have occurred through time between the Near East and Northern Africa115; 116. The number of children born in the period 1980 to 2005 with one or two African parents, See Suppl Figure 3, is compatible with the origin of the L hg persons being African. The L hgs are predominantly located in the Capital Region, Table 6, and in Metropolitan areas, Suppl Table 4, which is also compatible with recent immigration.

Immigration to Denmark increased from 1980, where 135,000 immigrants were registered, to 2005, where this number had risen to 345,000105. In the same period the number of descendants of immigrants, i.e. persons that might turn up in this study, rose from 18,000 to 109,000. Roughly 50 % of the immigrants were from western countries105, See Suppl Figure 3. This should give approximately 45,000 descendants of non-western immigrants over the time of study. This is within the order of magnitude to be expected from the frequency distribution of mtDNA hgs in Denmark.

There is thus a reasonable concordance between the suspected number of immigrants, from the temporal and structural study of mtDNA hgs, and the registered births of persons with non-Danish parents.

Whereas the temporal change in mtDNA distribution can be explained by immigration, it is more difficult to explain the spatial clines, Table 6 and Suppl Table 2. The frequency of hg-H is higher in

Northern Jutland than in other regions, particularly the Capital Region, and it is also higher in rural areas (p = 0.0001), Suppl Table 2. The difference cannot be explained by the comparatively much smaller differences in frequencies of hgs L and M. As the mobility of Danes was fairly restricted until around 190091, it may represent differences caused by centuries of relative isolation of a population north of Limfjorden. In Slovenia historically confirmed geographically-based sub-stratification of the

population117 has lead to extreme differences in mtDNA distributions.

A recent fine-scale gDNA population structure study from the UK46 revealed considerable

geographical heterogeneity and enabled the identification of specific sources of admixture from

continental Europe. A gDNA study of admixture in the Danish population118 showed considerably more homogeneity, but medieval admixture from Slavic tribes in North Germany, as well as a

North-South gradient were discernible. Both of these studies limited the participants to persons with local grand-parents, whereas our present study was not directed towards previous generations, but rather present-time and prospective. In addition to the gDNA/mtDNA interaction, the demonstrated spatio-temporal dynamics of the mt DNA hg distribution should be taken into account when designing studies of mtDNA associations with disease, physiological characteristics and phamacological effects. A prerequisite for genetic association studies is that the individuals are sampled from a homogenous population, or that cryptic population structure is corrected for, if not, false positive associations are likely119 120; 121.

Thus, when studying bi-genomic, i.e. both nucleo- and mito-genomic, disease associations, our results indicate that it is necessary to compensate for gDNA and mtDNA genetic stratification, as well as the interaction between the two sources of variation and spatio-temporal clines. To our knowledge, this has never been done, suggesting that previous reports on disease associations and mtDNA haplogroups should be considered preliminary.

Acknowledgements: The iPSYCH study was funded by The Lundbeck Foundation Initiative for Integrative Psychiatric

Research. We further gratefully acknowledge the financial support of The Jascha Foundation, The

Strategic Research Council (“Heart Safe”), The Augustinus Foundation, The Jascha Foundation, The

Lundbeck Foundation (Grant no. R67-A6552), and Familien Hede Nielsens Fond. This research has been conducted using the Danish National Biobank resource, supported by the Novo Nordisk

Foundation.

Legends to Tables and Figures

Table 1. Distribution of mtDNA macro hgs L0-L6, M, N and R. Incomplete is the number of

individuals that could not be haplotyped using the haplotyping algorithm (see Materials and

Methods).

Table 2. Distribution of mtDNA haplogroups constituting the macro-hg R. Incomplete is the number

of individuals that could not be haplotyped using the haplotyping algorithm (see Materials and

Methods).

Table 3. Distribution of mtDNA sub-haplogroups of H, V, J, T, K, and U, the most frequent European

haplogroups. HV could not be defined and is included in the H hg. Not sub-haplotyped is the

number of individuals that could not be subhaplotyped using the algorithm defined by the SNPs in

Suppl Table 1.

Table 4. Distribution of mtDNA haplogroups contained within the N and M macro hgs. Not

haplotyped is the number of individuals that could not be haplotyped using the algorithm defined

by the SNPs in Suppl Table 1.

Table 5. Geno-geographic affinity (ancestry) of the autosomal genome of persons in the study

broken down with respect to mtDNA macro-hg or hg. The number of persons in each geographical

group in the reference population is given in the header.

Table 6. Distribution of the most frequent haplogroups in the five administrative regions of

Denmark. Only persons born at locations with ≥ 20 births are included.

Table 7. Distributions of the most frequent haplogroups in the period 1981-1986 and 2000-2005.

Figure 1. Phylogenetic tree of mtDNA sequences, modified from (www.phylotree.org). MRCA: Most

recent common ancestor.

Figure 2. Map of Denmark with major administrative regions and metropolitan areas. The

population of each region or area, as well as the number of samples, is given on the map. Based on

birth-locations with ≥ 20 included individuals

Figure 3. A. PCA of the macro-haplogroups L, M, N and R. B. PCA of the macro-haplogroup R, i.e.

the major European haplogroups. C. PCA of the haplogroups belonging to macro-haplogroup N, and

D. macro-haplogroup M, and E. PCA of the haplogroups belonging to macro-haplogroup L, i.e. the

major African haplogroups. PC1: First principal component. PC2: Second principal component.

Figure 4. A. Median-joining (M-J) network of the macro-haplogroup R mtDNA sequences (2000 chosen at random). B. M-J network of the N and C. M haplogroups. D. M-J network of the L

haplogroup sequences.

Figure 5. The proportion of haplogroup L, M and U as a function of birth year.

Tables Table 1.

Macro-hg n % L0 26 0.1 L1 13 0.1 L2 54 0.2 L3 71 0.3 L4 16 0.1 L5 2 0.0 L6 0 0.0 M 394 1.6 N 1,700 7.0 R 21,940 90.6 Total haplotyped 24,216 100 Incomplete 426 1.7

Table 2. Hg n % H 10,912 50.0 U 3,237 14.8 T 2,323 10.6 J 2,195 10.1 K 1,754 8.0 V 785 3.6 R 570 2.6 F 50 0.2 B 10 0.0 Total haplotyped 21,836 100 Incomplete 104 0.5

Table 3

Hg Sub-hgs n % H H 3,924 36.4 H1-H30b-H79a 4,054 37.6 H2 1,058 9.8 H3 699 6.5 H4 220 2.0 H5-36 828 7.7 Number sub- 10,783 100 haplotyped Not sub-haplotyped 129 1.2 U U 120 3.8 U1 66 2.1 U2-U3 596 18.8 U4-9 603 19.0 U5a 1,113 35.1 U5b 471 14.8 U6 26 0.8 U7 87 2.7 U8 90 2.8 Number sub- 3,172 100 haplotyped Not sub-haplotyped 65 2.0 K K 1 0.1 K1 1,385 81.8 K2 307 18.1 K3 1 0.1 Number sub- 1,694 100 haplotyped Not sub-haplotyped 60 3.4 J J 2 0.1 J1 1,686 80.5 J2 407 19.4 Number sub- 2,095 100 haplotyped Not sub-haplotyped 100 4.6 T T* 9 0.4 T1 396 17.1 T2 1,908 82.5 Number sub- 2,313 100 haplotyped Not sub-haplotyped 10 0.4

Table 4.

Macro- (Sub) hgs n % hg N A 119 7.1 I 655 39.2 N 55 3.3 N1-5 111 6.6 N2 11 0.7 W 319 19.1 X 394 23.6 Y 6 0.4 Number 1,670 100 haplotyped Not haplotyped 30 1.8 M C 40 10.7 D4 53 14.2 D5 4 1.1 D6 2 0.5 E 8 2.1 G 10 2.7 M 81 21.7 M1 30 8.0 M2 11 2.9 M3 24 6.4 M30 14 3.7 M4 3 0.8 M5 26 7.0 M6 9 2.4 M7 37 9.9 M8a 7 1.9 M9 4 1.1 Z 11 2.9 Number 374 100 haplotyped Not haplotyped 20 5,1

29 bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Table 5.

Reference population (n) Haplogroup Denmark Greenland Europe Middle Africa America Central East Asia Oceania (716) (592) (161) East (127) (108) South (241) (39) (178) Asia (210) H* 90.8 0 6.9 1.4 0 0 0.8 0 0 H1 94.1 0 5.0 0.7 0 0 0.1 0 0 H2 94.5 0.1 4.8 0.3 0 0 0.3 0 0 H3 94.7 0 4.3 1.0 0 0 0 0 0 H4 90.0 0 7.7 2.3 0 0 0 0 0 H5´36 93.7 0 4.7 1.1 0 0 0.5 0 0 J* 0.0 0 50.0 50.0 0 0 0 0 0 J1 90.0 0.1 6.9 1.9 0 0 1.1 0.1 0 J2 89.4 0.2 5.9 4.2 0 0 0.2 0 0 T* 33.3 0 44.4 11.1 0 0 11.1 0 0 T1 84.6 0 7.8 4.5 0 0 3.0 0 0 T2 89.4 0.1 7.5 1.8 0 0.1 1.3 0 0 K 100 0 0 0 0 0 0 0 0 K1 91.8 0 6.4 1.0 0 0 0.8 0 0 K2 92.5 0 6.5 0 0 0 1.0 0 0 K3 100 0 0 0 0 0 0 0 0 U* 92.5 0 6.7 0 0 0 0.8 0 0 U1 38.2 0 17.6 29.4 0 0 14.7 0 0 U2-U3 80.6 0 7.9 5.4 0 0 6.2 0 0 U4-9 87.2 0 9.7 1.6 0 0 1.5 0 0 U5a 91.9 0 6.1 1.1 0 0 0.9 0 0 U5b 89.2 0 8.8 1.2 0 0 0.6 0.2 0 U6 42.3 0 3.8 53.8 0 0 0 0 0 U7 36.4 0 10.2 13.6 0 0 39.8 0 0 U8 86.0 0 8.6 2.2 0 0 3.2 0 0 Macro-hg L 4.5 0.0 14.6 70.8 8.4 0.0 1.7 0.0 0.0 M 0.6 9.9 29.9 9.0 0.0 0.3 42.2 8.1 0.0 N 80.4 1.1 11.7 3.8 0.0 0.1 2.3 0.5 0.0

Table 6.

Haplogroup Capital Region Zealand Central Region of North Denmark Region Denmark South Region Region Denmark % (n) % (n) % (n) % (n) %(n) H 43.4 (2,962) 46.9 (1,664) 45.7 (2,584) 45.6 (2,259) 48.8 (1,284) I 2.6 (175) 2.5 (89) 3.2 (180) 3.0 (148) 2.3 (60) J 8.6 (586) 8.8 (313) 9.4 (534) 9.4 (464) 8.2 (215) K 6.9 (468) 6.3 (224) 8.1 (456) 7.3 (364) 8.7 (229) L 1.3 (88) 0.5 (17) 0.5 (31) 0.4 (20) 0.6 (17) M 2.6 (176) 1.3 (45) 1.0 (56) 1.6 (79) 1.0 (27) N* 1.3 (92) 1.2 (43) 1.2 (68) 1.2 (61) 1.3 (33) R 3.6 (244) 2.1 (74) 2.5 (144) 2.4 (119) 2.3 (61) T 9.2 (627) 10.0 (354) 9.3 (525) 8.8 (434) 9.0 (238) U 13.8 (939) 13.8 (491) 13.1 (744) 13.4 (666) 13.1 (346) V 3.4 (229) 3.3 (116) 3.4 (195) 3.5 (175) 2.8 (73) W 1.4 (96) 1.6 (57) 1.3 (74) 1.4 (67) 0.9 (24) X 2.0 (134) 1.7 (60) 1.2 (67) 2.0 (101) 0.9 (24) NA 0 (2) 0.1 (3) 0 (2) 0 (1) 0 (1) Total: 100 (6,818) 100 (3,550) 100 (5,660) 100 (4,957) 100 (2,632)

Table 7. Haplogroup 1981-1986 2000-2005 P-Value % (n) % (n) H 47.3 (2,162) 44.3 (2,454) 2,98E-03 I 2.8 (130) 2.7 (150) 0,71 J 8.9 (405) 9.2 (511) 0,53 K 7.9 (359) 7.0 (387) 0,10 L 0.2 (8) 1.2 (67) 1,14E-10 M 1.0 (44) 2.4 (131) 3,73E-08 N* 1.1 (50) 1.3 (73) 0,32 R 2.2 (102) 2.9 (163) 0,03 T 9.8 (447) 8.5 (468) 0,02 U 12.7 (579) 14.3 (790) 0,02 V 3.6 (163) 3.1 (169) 0,16 W 1.2 (56) 1.4 (80) 0,39 X 1.4 (64) 1.6 (89) 0,41 NA 0.0 (1) 0.1 (3) 0,63 Total 100 (4,570) 100 (5,535) -

Figure 1.

Figure 2.

Figure 3. A

Figure 3. B

Figure 3. C

Figure 3. D

Figure 3. E

39 bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

40 bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

41 bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

42 bioRxiv preprint doi: https://doi.org/10.1101/148494; this version posted June 10, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 5.

2 Haplogroup% U % Haplogroup% L andM.

0 1981 1985 1990 1995 2000 2005

M L U

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